Vehicles include various components which degrade at different rates and have to be serviced at different times. In addition, the degradation rate of each component may be affected by multiple parameters, some of which are overlapping with other components while others are non-overlapping. For example, in hybrid electric vehicles, a system battery may degrade based on the rate of battery usage, the age of the battery, temperature conditions, the nature of the battery, etc. As another example, an air filter coupled to the engine intake may degrade based on the age of the filter, air quality, ambient weather conditions, etc.
Various approaches have been developed to predict the state of health of a vehicle component. One example approach is shown by Uchida in U.S. Pat. No. 8,676,4825. Therein the health of the battery of a hybrid vehicle is predicted based on a decrease in the fuel economy of the vehicle. Another example approach is shown by Kozlowski et al. in US 20030184307. Therein the state of health of a system battery is predicted based on the frequency of battery charging and discharging and its effects on battery parameters such as impedance, electrolyte state, etc. The battery health is then indicated in terms of a number of remaining useful cycles.
However the inventors herein have identified various issues with such approaches. As one example, the above approaches rely on statistical analyses that can be computationally intensive. Consequently, they may require extensive memory and processor resources to assess the health of the battery. As another example, the above approaches require frequent measurements via sensors coupled to the respective components. Reliance on sensors, which themselves are subject to wear and tear, can cause inaccuracy in the state of health estimation. In addition, the approach does not accurately account for the effect of temperature on the internal resistance and capacitance of the battery, as the battery ages. As yet another example, an operator may not be able to comprehend how much battery degradation has occurred when the battery health is indicated in terms of a number of remaining useful cycles. This may be particularly difficult when the battery is part of a hybrid vehicle where the engine automatically meets the driver demand when the battery is not able to. As a result, the vehicle operator may not be able to replace or service the battery before it is fully degraded, compromising vehicle operation. Further, the vehicle operator may not be able to timely modify their driving characteristics to avert battery degradation.
In one example, some of the above issues may be addressed by a method for a vehicle, comprising: predicting a state of degradation of a vehicle component based on a determined metric derived from a sensed vehicle operating parameter, including a past history of the determined metric; converting the predicted state of degradation into a remaining time or duration estimate for display to a vehicle operator based on the past driving history and predicted future driving, including the past history of the determined metric. In this way, the remaining useful life of a vehicle component may be more accurately predicted and the information may be conveyed to the vehicle operator in a more comprehensible manner. In one example, the vehicle component is a system battery.
As an example, a hybrid vehicle system may include a component whose life is predicted using statistical methods. A controller may predict a base rate of degradation of the component based on a past history (e.g., frequency) of servicing of the component. For example the controller may use a linear degradation model to predict a base value of the remaining life of the component. The controller may then update the estimate based on the nature of operation of the vehicle (e.g., the vehicle driving pattern and other driving statistics), the nature of operation of the component (e.g., how often the component was used in the current drive cycle, and responsive to which events), as well as any noise factors or parameters that may alter the base rate of degradation of the given component. As an example, when the component being assessed is a battery, the base rate of degradation may be based on when the battery was last serviced, battery state of charge, as well as temperature conditions. The model may use the measured parameters to estimate a current state of the battery's internal resistance and internal capacitance. The state of health of the battery is then calculated as a function of the estimated internal resistance and internal capacitance, a weightage assigned to the resistance and capacitance values varied based on the nature of the battery (e.g., based on whether the battery is a lead-acid battery or a lithium ion battery, etc.). In addition, the state of health of the battery may be updated based on how aggressively the vehicle was operated and any specific driving maneuvers (e.g., cornering maneuvers) that can rapidly drain the battery. In another example, when the component being assessed is an intake air filter, the base rate of degradation may be based on when the filter was last replaced, and the base rate may be updated based on sensed changes in manifold air flow at different degrees of throttle opening during engine transients, as well as ambient weather conditions that can cause a sudden clogging of the filter (e.g., presence of sudden dust storm or snow storm that can clog the filter). The sensed state of health may then be converted into an estimate of a remaining life of the component, including a time and/or distance of vehicle travel remaining before the component needs to be changed or serviced. The conversion may be based on the sensed state of health of the filter and further based on vehicle drive statistics including a time and/or distance of travel already completed by the vehicle, as well as operator driving patterns and habits. In one example, the remaining life of the battery may be used by the vehicle operator to infer if a hybrid vehicle can be started via the battery or not on a given vehicle start.
In this way, the remaining life of a vehicle component may be accurately predicted without relying on computationally intensive algorithms. By using data sensed on-board the vehicle, in association with vehicle driving statistics, the state of health of a component may be calculated more accurately. For example, the internal resistance and capacitance of a system battery may be better determined by accounting for temperature effects, as well as the effects of aggressive operator driving behavior. As another example, the degree of clogging of an air filter may be more accurately predicted based on a recursive estimation of mean and standard deviation of air flow values at large throttle openings. By assessing an air filter while relying on air flow or manifold pressure data sensed during vehicle transients, a larger portion of data collected over a vehicle drive cycle can be leveraged for filter prognostics. In addition, the need for actively holding the engine in a defined speed-load region, to complete a prognostic or diagnostic routine, is reduced. By converting the sensed state of health into an estimate of a remaining time or duration of vehicle operation before component servicing is required, a vehicle operator may be better notified of the condition of the component. As a result, timely component servicing may be ensured, improving vehicle performance. By predicting the remaining life of a vehicle component via a recursive estimation of statistical features, the remaining life of the component may be predicted with less computation intensity, without compromising on the accuracy of prediction. This enables a margin to be provided that better ensures healthy operation of the component for the estimated remaining life. The prognostics feature may provide an early indication of the remaining life the component to help a customer plan for maintenance ahead of time and avoid component failure. In addition, the convenience of online estimation may be provided in an easy to implement package. It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.